# How to integrate Bigml MCP with LlamaIndex

```json
{
  "title": "How to integrate Bigml MCP with LlamaIndex",
  "toolkit": "Bigml",
  "toolkit_slug": "bigml",
  "framework": "LlamaIndex",
  "framework_slug": "llama-index",
  "url": "https://composio.dev/toolkits/bigml/framework/llama-index",
  "markdown_url": "https://composio.dev/toolkits/bigml/framework/llama-index.md",
  "updated_at": "2026-05-12T10:03:04.870Z"
}
```

## Introduction

This guide walks you through connecting Bigml to LlamaIndex using the Composio tool router. By the end, you'll have a working Bigml agent that can create a new bigml project for customer data, list all correlations available in your account, get details for a specific bigml project through natural language commands.
This guide will help you understand how to give your LlamaIndex agent real control over a Bigml account through Composio's Bigml MCP server.
Before we dive in, let's take a quick look at the key ideas and tools involved.

## Also integrate Bigml with

- [OpenAI Agents SDK](https://composio.dev/toolkits/bigml/framework/open-ai-agents-sdk)
- [Claude Agent SDK](https://composio.dev/toolkits/bigml/framework/claude-agents-sdk)
- [Claude Code](https://composio.dev/toolkits/bigml/framework/claude-code)
- [Claude Cowork](https://composio.dev/toolkits/bigml/framework/claude-cowork)
- [Codex](https://composio.dev/toolkits/bigml/framework/codex)
- [OpenClaw](https://composio.dev/toolkits/bigml/framework/openclaw)
- [Hermes](https://composio.dev/toolkits/bigml/framework/hermes-agent)
- [CLI](https://composio.dev/toolkits/bigml/framework/cli)
- [Google ADK](https://composio.dev/toolkits/bigml/framework/google-adk)
- [LangChain](https://composio.dev/toolkits/bigml/framework/langchain)
- [Vercel AI SDK](https://composio.dev/toolkits/bigml/framework/ai-sdk)
- [Mastra AI](https://composio.dev/toolkits/bigml/framework/mastra-ai)
- [CrewAI](https://composio.dev/toolkits/bigml/framework/crew-ai)

## TL;DR

Here's what you'll learn:
- Set your OpenAI and Composio API keys
- Install LlamaIndex and Composio packages
- Create a Composio Tool Router session for Bigml
- Connect LlamaIndex to the Bigml MCP server
- Build a Bigml-powered agent using LlamaIndex
- Interact with Bigml through natural language

## What is LlamaIndex?

LlamaIndex is a data framework for building LLM applications. It provides tools for connecting LLMs to external data sources and services through agents and tools.
Key features include:
- ReAct Agent: Reasoning and acting pattern for tool-using agents
- MCP Tools: Native support for Model Context Protocol
- Context Management: Maintain conversation context across interactions
- Async Support: Built for async/await patterns

## What is the Bigml MCP server, and what's possible with it?

The Bigml MCP server is an implementation of the Model Context Protocol that connects your AI agent and assistants like Claude, Cursor, etc directly to your Bigml account. It provides structured and secure access to your machine learning environment, so your agent can perform actions like creating projects, managing data connectors, inspecting resources, and analyzing correlations on your behalf.
- Project creation and organization: Easily direct your agent to create new projects to group related BigML resources for streamlined workflows.
- External data connector management: Have your agent set up and retrieve external connectors to bring in data from external sources and databases.
- Resource inspection and retrieval: Let your agent fetch detailed metadata about projects or connectors, helping you monitor and audit your ML assets.
- Automated project cleanup: Instruct your agent to delete obsolete or unused projects, ensuring your workspace stays organized and efficient.
- Correlation browsing and analysis: Ask your agent to list and paginate correlation resources, uncovering relationships among your datasets for deeper insights.

## Supported Tools

| Tool slug | Name | Description |
|---|---|---|
| `BIGML_CREATE_EXTERNAL_CONNECTOR` | Create External Connector | Tool to create a new external connector for data sources. Use after configuring external databases or search indices. |
| `BIGML_CREATE_PROJECT` | Create Project | Tool to create a new project. Use when you need to group related BigML resources into a project. |
| `BIGML_DELETE_PROJECT` | Delete Project | Tool to delete an existing project. Use when you need to permanently remove a project resource after confirming it is not in use by other resources. |
| `BIGML_GET_CONFIGURATION` | Get Configuration | Retrieves complete details of a BigML configuration by its ID to get stored parameters. Configuration resources in BigML store parameters that can be reused across different resources. Use this action to inspect stored configuration parameters, verify configuration settings, check processing status, or retrieve metadata after creating a configuration. |
| `BIGML_GET_EXTERNAL_CONNECTOR` | Get External Connector | Retrieves complete details of a BigML external connector by its ID. External connectors enable BigML to import data from external databases (PostgreSQL, MySQL, SQL Server) or search engines (Elasticsearch). Use this action to inspect connection parameters, verify connector configuration, check processing status, or retrieve metadata after creating an external connector. |
| `BIGML_GET_PROJECT` | Get Project | Tool to retrieve details of a project by ID. Use when you need to inspect project metadata before analysis. |
| `BIGML_GET_SOURCE` | Get Source | Retrieves complete details of a BigML source by its ID. Use this to get information about the raw data and its parsing configuration. |
| `BIGML_LIST_ANOMALIES` | List Anomaly Detectors | Tool to list anomaly detector resources in your account. Use when you need to browse or paginate existing anomaly detectors with support for filtering, ordering, and pagination. |
| `BIGML_LIST_ANOMALY_SCORES` | List Anomaly Scores | Tool to list anomaly score resources. Use when you need to browse or paginate existing anomaly scores for the authenticated user. |
| `BIGML_LIST_ASSOCIATIONS` | List Associations | Tool to list association resources. Use when you need to browse or paginate existing associations for the authenticated user. |
| `BIGML_LIST_ASSOCIATION_SETS` | List Association Sets | Tool to list association set resources in your account. Use when you need to browse or paginate existing association sets with support for filtering, ordering, and pagination. |
| `BIGML_LIST_BATCH_ANOMALY_SCORES` | List Batch Anomaly Scores | Tool to list batch anomaly score resources. Use when you need to browse or paginate existing batch anomaly scores for the authenticated user. |
| `BIGML_LIST_BATCH_CENTROIDS` | List Batch Centroids | Tool to list all batch centroid resources in your account with support for filtering, ordering, and pagination. Use when you need to browse or retrieve existing batch centroids for the authenticated user. |
| `BIGML_LIST_BATCH_PREDICTIONS` | List Batch Predictions | Tool to list batch prediction resources. Use when you need to browse or paginate existing batch predictions for the authenticated user. |
| `BIGML_LIST_BATCH_PROJECTIONS` | List Batch Projections | Tool to list batch projection resources with support for filtering, ordering, and pagination. Use when you need to browse or retrieve batch projections for the authenticated account. |
| `BIGML_LIST_BATCH_TOPIC_DISTRIBUTIONS` | List Batch Topic Distributions | Tool to list batch topic distribution resources. Use when you need to browse or paginate existing batch topic distributions for the authenticated user. |
| `BIGML_LIST_CENTROIDS` | List Centroids | Tool to list centroid resources. Use when you need to browse or paginate existing centroids for the authenticated user. |
| `BIGML_LIST_CLUSTERS` | List Clusters | Tool to list cluster resources with support for filtering, ordering, and pagination. Use when you need to browse or paginate existing clusters for the authenticated user. |
| `BIGML_LIST_COMPOSITES` | List Composites | Tool to list composite source resources. Use when you need to browse or paginate existing composites for the authenticated user. |
| `BIGML_LIST_CONFIGURATIONS` | List Configurations | Tool to list all configuration resources in your account. Use when you need to browse or paginate existing configurations with support for filtering, ordering, and pagination. |
| `BIGML_LIST_CORRELATIONS` | List Correlations | Tool to list correlation resources. Use when you need to browse or paginate existing correlations for the authenticated user. |
| `BIGML_LIST_DATASETS` | List Datasets | Tool to list dataset resources. Use when you need to browse or paginate existing datasets for the authenticated user. |
| `BIGML_LIST_DEEPNETS` | List Deepnets | Tool to list deep neural network resources. Use when you need to browse or paginate existing deepnets for the authenticated user. |
| `BIGML_LIST_ENSEMBLES` | List Ensembles | Tool to list ensemble resources with filtering, ordering, and pagination support. Use when you need to browse or paginate existing ensembles for the authenticated user. |
| `BIGML_LIST_EVALUATIONS` | List Evaluations | Tool to list evaluation resources. Use when you need to browse or paginate existing evaluations for the authenticated user. |
| `BIGML_LIST_EXECUTIONS` | List Executions | Tool to list execution resources. Use when you need to browse or paginate existing executions for the authenticated user. |
| `BIGML_LIST_FORECASTS` | List Forecasts | Tool to list forecast resources. Use when you need to browse or paginate existing forecasts for the authenticated user. |
| `BIGML_LIST_FUSIONS` | List Fusions | Tool to list fusion resources. Use when you need to browse or paginate existing fusions for the authenticated user. |
| `BIGML_LIST_LIBRARIES` | List Libraries | Tool to list WhizzML library resources. Use when you need to browse or paginate existing libraries for the authenticated user. |
| `BIGML_LIST_LINEAR_REGRESSIONS` | List Linear Regressions | Tool to list linear regression resources. Use when you need to browse or paginate existing linear regressions for the authenticated user. |
| `BIGML_LIST_LOGISTIC_REGRESSIONS` | List Logistic Regressions | Tool to list logistic regression resources. Use when you need to browse or paginate existing logistic regressions for the authenticated user. |
| `BIGML_LIST_MODELS` | List Models | Tool to list model resources. Use when you need to browse or paginate existing models for the authenticated user. |
| `BIGML_LIST_OPTIMLS` | List OptiMLs | Tool to list OptiML resources in your account. Use when you need to browse or paginate existing OptiML models with support for filtering, ordering, and pagination. |
| `BIGML_LIST_PCAS` | List PCAs | Tool to list PCA resources. Use when you need to browse or paginate existing PCAs for the authenticated user. |
| `BIGML_LIST_PREDICTIONS` | List Predictions | Tool to list prediction resources. Use when you need to browse or paginate existing predictions for the authenticated user. |
| `BIGML_LIST_PROJECTIONS` | List Projections | Tool to list projection resources with support for filtering, ordering, and pagination. Use when you need to browse or retrieve projections for the authenticated account. |
| `BIGML_LIST_PROJECTS` | List Projects | Tool to list all project resources in your account with support for filtering, ordering, and pagination. Use when you need to browse or paginate existing projects. |
| `BIGML_LIST_SAMPLES` | List Samples | Tool to list sample resources. Use when you need to browse or paginate existing samples for the authenticated user. |
| `BIGML_LIST_SCRIPTS` | List Scripts | Tool to list WhizzML script resources. Use when you need to browse or paginate existing scripts for the authenticated user. |
| `BIGML_LIST_SOURCES` | List Sources | Tool to list source resources in your account. Use when you need to browse or paginate existing data sources with support for filtering, ordering, and pagination. |
| `BIGML_LIST_STATISTICAL_TESTS` | List Statistical Tests | Tool to list statistical test resources. Use when you need to browse or paginate existing statistical tests for the authenticated user. |
| `BIGML_LIST_TIMESERIES` | List Time Series | Tool to list time series resources. Use when you need to browse or paginate existing time series for the authenticated user. |
| `BIGML_LIST_TOPIC_DISTRIBUTIONS` | List Topic Distributions | Tool to list topic distribution resources. Use when you need to browse or paginate existing topic distributions for the authenticated user. |
| `BIGML_LIST_TOPIC_MODELS` | List Topic Models | Tool to list topic model resources. Use when you need to browse or paginate existing topic models for the authenticated user. |
| `BIGML_UPDATE_SOURCE` | Update Source | Tool to update a source's name, description, tags, or parsing configuration. Use when you need to modify source metadata or field properties. |

## Supported Triggers

None listed.

## Creating MCP Server - Stand-alone vs Composio SDK

The Bigml MCP server is an implementation of the Model Context Protocol that connects your AI agent to Bigml. It provides structured and secure access so your agent can perform Bigml operations on your behalf through a secure, permission-based interface.
With Composio's managed implementation, you don't have to create your own developer app. For production, if you're building an end product, we recommend using your own credentials. The managed server helps you prototype fast and go from 0-1 faster.

## Step-by-step Guide

### 1. Prerequisites

Before you begin, make sure you have:
- Python 3.8/Node 16 or higher installed
- A Composio account with the API key
- An OpenAI API key
- A Bigml account and project
- Basic familiarity with async Python/Typescript

### 1. Getting API Keys for OpenAI, Composio, and Bigml

No description provided.

### 2. Installing dependencies

No description provided.
```python
pip install composio-llamaindex llama-index llama-index-llms-openai llama-index-tools-mcp python-dotenv
```

```typescript
npm install @composio/llamaindex @llamaindex/openai @llamaindex/tools @llamaindex/workflow dotenv
```

### 3. Set environment variables

Create a .env file in your project root:
These credentials will be used to:
- Authenticate with OpenAI's GPT-5 model
- Connect to Composio's Tool Router
- Identify your Composio user session for Bigml access
```bash
OPENAI_API_KEY=your-openai-api-key
COMPOSIO_API_KEY=your-composio-api-key
COMPOSIO_USER_ID=your-user-id
```

### 4. Import modules

No description provided.
```python
import asyncio
import os
import dotenv

from composio import Composio
from composio_llamaindex import LlamaIndexProvider
from llama_index.core.agent.workflow import ReActAgent
from llama_index.core.workflow import Context
from llama_index.llms.openai import OpenAI
from llama_index.tools.mcp import BasicMCPClient, McpToolSpec

dotenv.load_dotenv()
```

```typescript
import "dotenv/config";
import readline from "node:readline/promises";
import { stdin as input, stdout as output } from "node:process";

import { Composio } from "@composio/core";

import { mcp } from "@llamaindex/tools";
import { agent as createAgent } from "@llamaindex/workflow";
import { openai } from "@llamaindex/openai";

dotenv.config();
```

### 5. Load environment variables and initialize Composio

No description provided.
```python
OPENAI_API_KEY = os.getenv("OPENAI_API_KEY")
COMPOSIO_API_KEY = os.getenv("COMPOSIO_API_KEY")
COMPOSIO_USER_ID = os.getenv("COMPOSIO_USER_ID")

if not OPENAI_API_KEY:
    raise ValueError("OPENAI_API_KEY is not set in the environment")
if not COMPOSIO_API_KEY:
    raise ValueError("COMPOSIO_API_KEY is not set in the environment")
if not COMPOSIO_USER_ID:
    raise ValueError("COMPOSIO_USER_ID is not set in the environment")
```

```typescript
const OPENAI_API_KEY = process.env.OPENAI_API_KEY;
const COMPOSIO_API_KEY = process.env.COMPOSIO_API_KEY;
const COMPOSIO_USER_ID = process.env.COMPOSIO_USER_ID;

if (!OPENAI_API_KEY) throw new Error("OPENAI_API_KEY is not set");
if (!COMPOSIO_API_KEY) throw new Error("COMPOSIO_API_KEY is not set");
if (!COMPOSIO_USER_ID) throw new Error("COMPOSIO_USER_ID is not set");
```

### 6. Create a Tool Router session and build the agent function

What's happening here:
- We create a Composio client using your API key and configure it with the LlamaIndex provider
- We then create a tool router MCP session for your user, specifying the toolkits we want to use (in this case, bigml)
- The session returns an MCP HTTP endpoint URL that acts as a gateway to all your configured tools
- LlamaIndex will connect to this endpoint to dynamically discover and use the available Bigml tools.
- The MCP tools are mapped to LlamaIndex-compatible tools and plug them into the Agent.
```python
async def build_agent() -> ReActAgent:
    composio_client = Composio(
        api_key=COMPOSIO_API_KEY,
        provider=LlamaIndexProvider(),
    )

    session = composio_client.create(
        user_id=COMPOSIO_USER_ID,
        toolkits=["bigml"],
    )

    mcp_url = session.mcp.url
    print(f"Composio MCP URL: {mcp_url}")

    mcp_client = BasicMCPClient(mcp_url, headers={"x-api-key": COMPOSIO_API_KEY})
    mcp_tool_spec = McpToolSpec(client=mcp_client)
    tools = await mcp_tool_spec.to_tool_list_async()

    llm = OpenAI(model="gpt-5")

    description = "An agent that uses Composio Tool Router MCP tools to perform Bigml actions."
    system_prompt = """
    You are a helpful assistant connected to Composio Tool Router.
    Use the available tools to answer user queries and perform Bigml actions.
    """
    return ReActAgent(tools=tools, llm=llm, description=description, system_prompt=system_prompt, verbose=True)
```

```typescript
async function buildAgent() {

  console.log(`Initializing Composio client...${COMPOSIO_USER_ID!}...`);
  console.log(`COMPOSIO_USER_ID: ${COMPOSIO_USER_ID!}...`);

  const composio = new Composio({
    apiKey: COMPOSIO_API_KEY,
    provider: new LlamaindexProvider(),
  });

  const session = await composio.create(
    COMPOSIO_USER_ID!,
    {
      toolkits: ["bigml"],
    },
  );

  const mcpUrl = session.mcp.url;
  console.log(`Composio Tool Router MCP URL: ${mcpUrl}`);

  const server = mcp({
    url: mcpUrl,
    clientName: "composio_tool_router_with_llamaindex",
    requestInit: {
      headers: {
        "x-api-key": COMPOSIO_API_KEY!,
      },
    },
    // verbose: true,
  });

  const tools = await server.tools();

  const llm = openai({ apiKey: OPENAI_API_KEY, model: "gpt-5" });

  const agent = createAgent({
    name: "composio_tool_router_with_llamaindex",
        description : "An agent that uses Composio Tool Router MCP tools to perform actions.",
    systemPrompt:
      "You are a helpful assistant connected to Composio Tool Router."+
"Use the available tools to answer user queries and perform Bigml actions." ,
    llm,
    tools,
  });

  return agent;
}
```

### 7. Create an interactive chat loop

No description provided.
```python
async def chat_loop(agent: ReActAgent) -> None:
    ctx = Context(agent)
    print("Type 'quit', 'exit', or Ctrl+C to stop.")

    while True:
        try:
            user_input = input("\nYou: ").strip()
        except (KeyboardInterrupt, EOFError):
            print("\nBye!")
            break

        if not user_input or user_input.lower() in {"quit", "exit"}:
            print("Bye!")
            break

        try:
            print("Agent: ", end="", flush=True)
            handler = agent.run(user_input, ctx=ctx)

            async for event in handler.stream_events():
                # Stream token-by-token from LLM responses
                if hasattr(event, "delta") and event.delta:
                    print(event.delta, end="", flush=True)
                # Show tool calls as they happen
                elif hasattr(event, "tool_name"):
                    print(f"\n[Using tool: {event.tool_name}]", flush=True)

            # Get final response
            response = await handler
            print()  # Newline after streaming
        except KeyboardInterrupt:
            print("\n[Interrupted]")
            continue
        except Exception as e:
            print(f"\nError: {e}")
```

```typescript
async function chatLoop(agent: ReturnType<typeof createAgent>) {
  const rl = readline.createInterface({ input, output });

  console.log("Type 'quit' or 'exit' to stop.");

  while (true) {
    let userInput: string;

    try {
      userInput = (await rl.question("\nYou: ")).trim();
    } catch {
      console.log("\nAgent: Bye!");
      break;
    }

    if (!userInput) {
      continue;
    }

    const lower = userInput.toLowerCase();
    if (lower === "quit" || lower === "exit") {
      console.log("Agent: Bye!");
      break;
    }

    try {
      process.stdout.write("Agent: ");

      const stream = agent.runStream(userInput);
      let finalResult: any = null;

      for await (const event of stream) {
        // The event.data contains the streamed content
        const data: any = event.data;

        // Check for streaming delta content
        if (data?.delta) {
          process.stdout.write(data.delta);
        }

        // Store final result for fallback
        if (data?.result || data?.message) {
          finalResult = data;
        }
      }

      // If no streaming happened, show the final result
      if (finalResult) {
        const answer =
          finalResult.result ??
          finalResult.message?.content ??
          finalResult.message ??
          "";
        if (answer && typeof answer === "string" && !answer.includes("[object")) {
          process.stdout.write(answer);
        }
      }

      console.log(); // New line after streaming completes
    } catch (err: any) {
      console.error("\nAgent error:", err?.message ?? err);
    }
  }

  rl.close();
}
```

### 8. Define the main entry point

What's happening here:
- We're orchestrating the entire application flow
- The agent gets built with proper error handling
- Then we kick off the interactive chat loop so you can start talking to Bigml
```python
async def main() -> None:
    agent = await build_agent()
    await chat_loop(agent)

if __name__ == "__main__":
    # Handle Ctrl+C gracefully
    signal.signal(signal.SIGINT, lambda s, f: (print("\nBye!"), exit(0)))
    try:
        asyncio.run(main())
    except KeyboardInterrupt:
        print("\nBye!")
```

```typescript
async function main() {
  try {
    const agent = await buildAgent();
    await chatLoop(agent);
  } catch (err) {
    console.error("Failed to start agent:", err);
    process.exit(1);
  }
}

main();
```

### 9. Run the agent

When prompted, authenticate and authorise your agent with Bigml, then start asking questions.
```bash
python llamaindex_agent.py
```

```typescript
npx ts-node llamaindex-agent.ts
```

## Complete Code

```python
import asyncio
import os
import signal
import dotenv

from composio import Composio
from composio_llamaindex import LlamaIndexProvider
from llama_index.core.agent.workflow import ReActAgent
from llama_index.core.workflow import Context
from llama_index.llms.openai import OpenAI
from llama_index.tools.mcp import BasicMCPClient, McpToolSpec

dotenv.load_dotenv()

OPENAI_API_KEY = os.getenv("OPENAI_API_KEY")
COMPOSIO_API_KEY = os.getenv("COMPOSIO_API_KEY")
COMPOSIO_USER_ID = os.getenv("COMPOSIO_USER_ID")

if not OPENAI_API_KEY:
    raise ValueError("OPENAI_API_KEY is not set")
if not COMPOSIO_API_KEY:
    raise ValueError("COMPOSIO_API_KEY is not set")
if not COMPOSIO_USER_ID:
    raise ValueError("COMPOSIO_USER_ID is not set")

async def build_agent() -> ReActAgent:
    composio_client = Composio(
        api_key=COMPOSIO_API_KEY,
        provider=LlamaIndexProvider(),
    )

    session = composio_client.create(
        user_id=COMPOSIO_USER_ID,
        toolkits=["bigml"],
    )

    mcp_url = session.mcp.url
    print(f"Composio MCP URL: {mcp_url}")

    mcp_client = BasicMCPClient(mcp_url, headers={"x-api-key": COMPOSIO_API_KEY})
    mcp_tool_spec = McpToolSpec(client=mcp_client)
    tools = await mcp_tool_spec.to_tool_list_async()

    llm = OpenAI(model="gpt-5")
    description = "An agent that uses Composio Tool Router MCP tools to perform Bigml actions."
    system_prompt = """
    You are a helpful assistant connected to Composio Tool Router.
    Use the available tools to answer user queries and perform Bigml actions.
    """
    return ReActAgent(
        tools=tools,
        llm=llm,
        description=description,
        system_prompt=system_prompt,
        verbose=True,
    );

async def chat_loop(agent: ReActAgent) -> None:
    ctx = Context(agent)
    print("Type 'quit', 'exit', or Ctrl+C to stop.")

    while True:
        try:
            user_input = input("\nYou: ").strip()
        except (KeyboardInterrupt, EOFError):
            print("\nBye!")
            break

        if not user_input or user_input.lower() in {"quit", "exit"}:
            print("Bye!")
            break

        try:
            print("Agent: ", end="", flush=True)
            handler = agent.run(user_input, ctx=ctx)

            async for event in handler.stream_events():
                # Stream token-by-token from LLM responses
                if hasattr(event, "delta") and event.delta:
                    print(event.delta, end="", flush=True)
                # Show tool calls as they happen
                elif hasattr(event, "tool_name"):
                    print(f"\n[Using tool: {event.tool_name}]", flush=True)

            # Get final response
            response = await handler
            print()  # Newline after streaming
        except KeyboardInterrupt:
            print("\n[Interrupted]")
            continue
        except Exception as e:
            print(f"\nError: {e}")

async def main() -> None:
    agent = await build_agent()
    await chat_loop(agent)

if __name__ == "__main__":
    # Handle Ctrl+C gracefully
    signal.signal(signal.SIGINT, lambda s, f: (print("\nBye!"), exit(0)))
    try:
        asyncio.run(main())
    except KeyboardInterrupt:
        print("\nBye!")
```

```typescript
import "dotenv/config";
import readline from "node:readline/promises";
import { stdin as input, stdout as output } from "node:process";

import { Composio } from "@composio/core";
import { LlamaindexProvider } from "@composio/llamaindex";

import { mcp } from "@llamaindex/tools";
import { agent as createAgent } from "@llamaindex/workflow";
import { openai } from "@llamaindex/openai";

dotenv.config();

const OPENAI_API_KEY = process.env.OPENAI_API_KEY;
const COMPOSIO_API_KEY = process.env.COMPOSIO_API_KEY;
const COMPOSIO_USER_ID = process.env.COMPOSIO_USER_ID;

if (!OPENAI_API_KEY) {
    throw new Error("OPENAI_API_KEY is not set in the environment");
  }
if (!COMPOSIO_API_KEY) {
    throw new Error("COMPOSIO_API_KEY is not set in the environment");
  }
if (!COMPOSIO_USER_ID) {
    throw new Error("COMPOSIO_USER_ID is not set in the environment");
  }

async function buildAgent() {

  console.log(`Initializing Composio client...${COMPOSIO_USER_ID!}...`);
  console.log(`COMPOSIO_USER_ID: ${COMPOSIO_USER_ID!}...`);

  const composio = new Composio({
    apiKey: COMPOSIO_API_KEY,
    provider: new LlamaindexProvider(),
  });

  const session = await composio.create(
    COMPOSIO_USER_ID!,
    {
      toolkits: ["bigml"],
    },
  );

  const mcpUrl = session.mcp.url;
  console.log(`Composio Tool Router MCP URL: ${mcpUrl}`);

  const server = mcp({
    url: mcpUrl,
    clientName: "composio_tool_router_with_llamaindex",
    requestInit: {
      headers: {
        "x-api-key": COMPOSIO_API_KEY!,
      },
    },
    // verbose: true,
  });

  const tools = await server.tools();

  const llm = openai({ apiKey: OPENAI_API_KEY, model: "gpt-5" });

  const agent = createAgent({
    name: "composio_tool_router_with_llamaindex",
    description:
      "An agent that uses Composio Tool Router MCP tools to perform actions.",
    systemPrompt:
      "You are a helpful assistant connected to Composio Tool Router."+
"Use the available tools to answer user queries and perform Bigml actions." ,
    llm,
    tools,
  });

  return agent;
}

async function chatLoop(agent: ReturnType<typeof createAgent>) {
  const rl = readline.createInterface({ input, output });

  console.log("Type 'quit' or 'exit' to stop.");

  while (true) {
    let userInput: string;

    try {
      userInput = (await rl.question("\nYou: ")).trim();
    } catch {
      console.log("\nAgent: Bye!");
      break;
    }

    if (!userInput) {
      continue;
    }

    const lower = userInput.toLowerCase();
    if (lower === "quit" || lower === "exit") {
      console.log("Agent: Bye!");
      break;
    }

    try {
      process.stdout.write("Agent: ");

      const stream = agent.runStream(userInput);
      let finalResult: any = null;

      for await (const event of stream) {
        // The event.data contains the streamed content
        const data: any = event.data;

        // Check for streaming delta content
        if (data?.delta) {
          process.stdout.write(data.delta);
        }

        // Store final result for fallback
        if (data?.result || data?.message) {
          finalResult = data;
        }
      }

      // If no streaming happened, show the final result
      if (finalResult) {
        const answer =
          finalResult.result ??
          finalResult.message?.content ??
          finalResult.message ??
          "";
        if (answer && typeof answer === "string" && !answer.includes("[object")) {
          process.stdout.write(answer);
        }
      }

      console.log(); // New line after streaming completes
    } catch (err: any) {
      console.error("\nAgent error:", err?.message ?? err);
    }
  }

  rl.close();
}

async function main() {
  try {
    const agent = await buildAgent();
    await chatLoop(agent);
  } catch (err: any) {
    console.error("Failed to start agent:", err?.message ?? err);
    process.exit(1);
  }
}

main();
```

## Conclusion

You've successfully connected Bigml to LlamaIndex through Composio's Tool Router MCP layer.
Key takeaways:
- Tool Router dynamically exposes Bigml tools through an MCP endpoint
- LlamaIndex's ReActAgent handles reasoning and orchestration; Composio handles integrations
- The agent becomes more capable without increasing prompt size
- Async Python provides clean, efficient execution of agent workflows
You can easily extend this to other toolkits like Gmail, Notion, Stripe, GitHub, and more by adding them to the toolkits parameter.

## How to build Bigml MCP Agent with another framework

- [OpenAI Agents SDK](https://composio.dev/toolkits/bigml/framework/open-ai-agents-sdk)
- [Claude Agent SDK](https://composio.dev/toolkits/bigml/framework/claude-agents-sdk)
- [Claude Code](https://composio.dev/toolkits/bigml/framework/claude-code)
- [Claude Cowork](https://composio.dev/toolkits/bigml/framework/claude-cowork)
- [Codex](https://composio.dev/toolkits/bigml/framework/codex)
- [OpenClaw](https://composio.dev/toolkits/bigml/framework/openclaw)
- [Hermes](https://composio.dev/toolkits/bigml/framework/hermes-agent)
- [CLI](https://composio.dev/toolkits/bigml/framework/cli)
- [Google ADK](https://composio.dev/toolkits/bigml/framework/google-adk)
- [LangChain](https://composio.dev/toolkits/bigml/framework/langchain)
- [Vercel AI SDK](https://composio.dev/toolkits/bigml/framework/ai-sdk)
- [Mastra AI](https://composio.dev/toolkits/bigml/framework/mastra-ai)
- [CrewAI](https://composio.dev/toolkits/bigml/framework/crew-ai)

## Related Toolkits

- [Composio](https://composio.dev/toolkits/composio) - Composio is an integration platform that connects AI agents with hundreds of business tools. It streamlines authentication and lets you trigger actions across services—no custom code needed.
- [Composio search](https://composio.dev/toolkits/composio_search) - Composio search is a unified web search toolkit spanning travel, e-commerce, news, financial markets, images, and more. It lets you and your apps tap into up-to-date web data from a single, easy-to-integrate service.
- [Perplexityai](https://composio.dev/toolkits/perplexityai) - Perplexityai delivers natural, conversational AI models for generating human-like text. Instantly get context-aware, high-quality responses for chat, search, or complex workflows.
- [Browser tool](https://composio.dev/toolkits/browser_tool) - Browser tool is a virtual browser integration that lets AI agents interact with the web programmatically. It enables automated browsing, scraping, and action-taking from any AI workflow.
- [Ai ml api](https://composio.dev/toolkits/ai_ml_api) - Ai ml api is a suite of AI/ML models for natural language and image tasks. It provides fast, scalable access to advanced AI capabilities for your apps and workflows.
- [Aivoov](https://composio.dev/toolkits/aivoov) - Aivoov is an AI-powered text-to-speech platform offering 1,000+ voices in over 150 languages. Instantly turn written content into natural, human-like audio for any application.
- [All images ai](https://composio.dev/toolkits/all_images_ai) - All-Images.ai is an AI-powered image generation and management platform. It helps you create, search, and organize images effortlessly with advanced AI capabilities.
- [Anthropic administrator](https://composio.dev/toolkits/anthropic_administrator) - Anthropic administrator is an API for managing Anthropic organizational resources like members, workspaces, and API keys. It helps you automate admin tasks and streamline resource management across your Anthropic organization.
- [Api labz](https://composio.dev/toolkits/api_labz) - Api labz is a platform offering a suite of AI-driven APIs and workflow tools. It helps developers automate tasks and build smarter, more efficient applications.
- [Apipie ai](https://composio.dev/toolkits/apipie_ai) - Apipie ai is an AI model aggregator offering a single API for accessing top AI models from multiple providers. It helps developers build cost-efficient, latency-optimized AI solutions without juggling multiple integrations.
- [Astica ai](https://composio.dev/toolkits/astica_ai) - Astica ai provides APIs for computer vision, NLP, and voice synthesis. Integrate advanced AI features into your app with a single API key.
- [Botbaba](https://composio.dev/toolkits/botbaba) - Botbaba is a platform for building, managing, and deploying conversational AI chatbots across messaging channels. It streamlines chatbot automation, making it easier to integrate AI into customer interactions.
- [Botpress](https://composio.dev/toolkits/botpress) - Botpress is an open-source platform for building, deploying, and managing chatbots. It helps teams automate conversations and deliver rich, interactive messaging experiences.
- [Chatbotkit](https://composio.dev/toolkits/chatbotkit) - Chatbotkit is a platform for building and managing AI-powered chatbots using robust APIs and SDKs. It lets you easily add conversational AI to your apps for better user engagement.
- [Cody](https://composio.dev/toolkits/cody) - Cody is an AI assistant built for businesses, trained on your company's knowledge and data. It delivers instant answers and insights, tailored for your team.
- [Context7 MCP](https://composio.dev/toolkits/context7_mcp) - Context7 MCP delivers live, version-specific code docs and examples right from the source. It helps developers and AI agents instantly retrieve authoritative programming info—no more out-of-date docs.
- [Customgpt](https://composio.dev/toolkits/customgpt) - CustomGPT.ai lets you build and deploy chatbots tailored to your own data and business needs. Get precise and context-aware AI conversations without writing code.
- [Datarobot](https://composio.dev/toolkits/datarobot) - Datarobot is a machine learning platform that automates model development, deployment, and monitoring. It empowers organizations to quickly gain predictive insights from large datasets.
- [Deepgram](https://composio.dev/toolkits/deepgram) - Deepgram is an AI-powered speech recognition platform for accurate audio transcription and understanding. It enables fast, scalable speech-to-text with advanced audio intelligence features.
- [DeepImage](https://composio.dev/toolkits/deepimage) - DeepImage is an AI-powered image enhancer and upscaler. Get higher-quality images with just a few clicks.

## Frequently Asked Questions

### What are the differences in Tool Router MCP and Bigml MCP?

With a standalone Bigml MCP server, the agents and LLMs can only access a fixed set of Bigml tools tied to that server. However, with the Composio Tool Router, agents can dynamically load tools from Bigml and many other apps based on the task at hand, all through a single MCP endpoint.

### Can I use Tool Router MCP with LlamaIndex?

Yes, you can. LlamaIndex fully supports MCP integration. You get structured tool calling, message history handling, and model orchestration while Tool Router takes care of discovering and serving the right Bigml tools.

### Can I manage the permissions and scopes for Bigml while using Tool Router?

Yes, absolutely. You can configure which Bigml scopes and actions are allowed when connecting your account to Composio. You can also bring your own OAuth credentials or API configuration so you keep full control over what the agent can do.

### How safe is my data with Composio Tool Router?

All sensitive data such as tokens, keys, and configuration is fully encrypted at rest and in transit. Composio is SOC 2 Type 2 compliant and follows strict security practices so your Bigml data and credentials are handled as safely as possible.

---
[See all toolkits](https://composio.dev/toolkits) · [Composio docs](https://docs.composio.dev/llms.txt)